
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "21"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 81 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 77 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.063641	-0.184712	36.012276	9.753330	13.320997	3.437066	43.208654	12.630482	0.7466	0.7644	0.2672	10.551285	11.481421
2459593	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.851858	0.826249	39.779200	11.630933	5.347234	3.298334	12.200605	2.737357	0.6944	0.7000	0.2554	4.167672	3.989915
2459592	dish_ok	100.00%	100.00%	100.00%	0.00%	-	-	1.043554	-0.827861	6.114483	2.964681	-0.554223	-0.246492	-1.564465	0.122491	0.0285	0.0282	0.0013	nan	nan
2459591	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.116973	1.646411	46.662092	13.232236	3.434974	4.251026	1.491283	0.368571	0.6239	0.6543	0.3817	2.992152	3.234395
2459590	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.423165	1.078502	42.385388	11.655337	2.232042	4.340906	2.310514	0.387026	0.6273	0.6536	0.3779	3.894480	4.174332
2459589	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.022192	0.600196	37.680037	10.294446	13.830455	3.447485	23.310064	5.469051	0.6529	0.6702	0.3128	3.345672	3.713361
2459588	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	226.950934	223.554336	inf	inf	23.636780	26.891296	19.236591	18.001917	nan	nan	nan	0.000000	0.000000
2459587	dish_ok	100.00%	-	-	-	100.00%	0.00%	352.694037	350.954891	inf	inf	104.895598	107.891446	575.868624	564.877500	nan	nan	nan	0.000000	0.000000
2459586	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.135192	0.395787	4.174220	0.820964	7.916409	1.297007	1.980747	1.416174	0.6491	0.6541	0.3694	3.704538	3.560107
2459585	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.081882	0.990995	7.174064	2.189746	5.627691	0.630065	2.217104	1.059303	0.0314	0.0320	0.0030	0.000000	0.000000
2459584	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	20.846133	19.625464	13.496499	3.583852	49.737184	13.321296	-0.352796	-0.056211	0.5972	0.6128	0.3724	13.757470	15.608135
2459583	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.972310	2.353083	16.037190	2.870986	0.910667	2.463208	0.430155	3.823280	0.6176	0.6400	0.3922	3.686332	3.774165
2459582	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.494630	1.316529	14.117300	2.607099	1.717948	3.037563	0.094438	1.928823	0.6296	0.6530	0.3928	3.306327	3.462168
2459581	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459580	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.754786	1.769519	16.062333	2.467487	1.325794	2.635364	0.170895	2.465496	0.6331	0.6555	0.3876	3.850686	3.902926
2459579	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.526431	0.763199	18.557568	3.492687	0.632991	2.492757	0.991101	1.883717	0.6305	0.6592	0.3943	3.308325	3.699424
2459578	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	1.064998	-0.594821	2.241285	-0.033423	-0.269666	-0.133715	1.908571	0.125899	0.0283	0.0300	0.0021	nan	nan
2459577	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.202680	1.954952	14.986333	2.224239	2.487993	1.898261	-0.035919	0.104277	0.6319	0.6579	0.3893	3.314762	3.487209
2459576	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.867673	1.650276	11.608517	1.973164	2.157756	1.322143	0.875599	0.307972	0.6545	0.6741	0.3848	2.994735	3.142940
2459575	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.283782	0.846290	11.218582	2.070756	13.386893	2.239452	21.870855	2.779176	0.7180	0.7303	0.3399	0.000000	0.000000
2459574	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.836131	0.689033	11.266056	1.826970	0.948390	1.953312	1.806355	-0.270601	0.6570	0.6805	0.3747	3.114414	3.194910
2459573	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.501318	1.822972	20.154999	3.574143	0.030666	2.189423	0.746431	2.554655	0.6329	0.6561	0.3957	0.000000	0.000000
2459572	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.180019	1.780489	11.282832	1.817127	1.091368	1.147585	1.856274	0.907423	0.6393	0.6582	0.3941	2.910350	2.777330
2459571	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.600907	-0.527343	2.069896	-0.097367	-0.929942	-0.896748	1.645850	0.195146	0.0297	0.0313	0.0028	nan	nan
2459570	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.164538	7.558830	21.504918	10.926015	6.628714	6.880000	5.440909	4.942547	0.6345	0.6670	0.3753	5.436811	5.316860
2459569	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.480633	0.538005	13.071454	2.235277	21.102231	2.228190	20.265937	3.106619	0.7143	0.7288	0.2575	3.642905	3.965304
2459566	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.404705	0.984668	15.140526	2.660287	2.609613	2.211693	-0.034350	-0.257032	0.6413	0.6662	0.4043	9.217382	1.817634
2459565	dish_ok	0.00%	-	-	-	100.00%	0.00%	0.000000	0.000000	-0.016407	-0.016407	-0.758305	-0.758305	-0.875717	-0.875717	nan	nan	nan	0.000000	0.000000
2459564	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.862351	1.185492	-1.217256	-0.011690	0.123856	-1.364740	-0.149673	-1.398211	0.6824	0.6722	0.4047	2.613324	2.475150
2459563	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459562	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.500405	-0.032527	-0.425078	-0.547172	1.061666	-2.275135	0.109817	-1.091003	0.6787	0.6743	0.3956	2.853194	2.590925
2459561	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.679046	0.850570	-0.446047	-0.063330	1.046949	-2.058236	0.613536	-1.391437	0.6798	0.6738	0.3924	3.205115	2.837982
2459560	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.772679	0.701011	-0.277167	-0.215106	1.155463	-1.351550	0.516822	-0.817629	0.6770	0.6665	0.4051	3.162522	2.819402
2459559	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.307998	1.154206	-0.526869	0.289688	1.083300	-0.966888	0.372064	-1.136794	0.6768	0.6723	0.4013	2.482974	2.312997
2459558	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.120764	4.755477	-0.402567	9.025605	0.948297	8.587737	0.407310	-1.254825	0.6748	0.6637	0.4091	2.708114	2.603167
2459557	dish_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0316	0.0308	0.0026	nan	nan
2459556	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.165516	4.789003	-0.321129	9.350291	1.300545	7.968702	0.582089	-2.426304	0.6803	0.6751	0.3983	2.751549	2.677685
2459553	dish_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6780	0.6679	0.4131	nan	nan
2459552	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.617907	5.564607	0.069220	13.275096	0.765416	15.159166	0.324373	0.200663	0.6819	0.6726	0.4128	0.000000	0.000000
2459551	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.358707	0.474375	-0.662341	-0.106865	1.434946	-1.876193	0.375725	-1.412696	0.6710	0.6692	0.4094	2.598604	2.471889
2459550	dish_ok	-	97.53%	97.53%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0523	0.0523	0.0016	nan	nan
2459549	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459542	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.424011	4.068598	-0.365162	19.463290	-0.558394	0.735636	0.285630	-1.591363	0.7342	0.7221	0.3768	2.946257	2.736821
2459541	dish_ok	100.00%	1.79%	1.79%	0.00%	100.00%	0.00%	2.496652	3.523704	0.260805	11.409487	-0.086781	8.761045	0.053189	3.109759	0.7199	0.7017	0.3521	3.899615	3.420875
2459540	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.160155	1.613568	-0.642121	1.090357	0.848119	-0.381292	-0.014218	-0.942472	0.7326	0.7074	0.3717	2.679160	2.567464
2459536	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.103756	6.586355	0.224541	16.488257	1.326446	15.720782	0.142189	-2.800515	0.7069	0.6982	0.4270	2.671645	2.364257
2459535	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.710229	1.498825	-0.359134	0.892657	0.298116	0.094324	0.105473	-1.003257	0.7594	0.7263	0.3876	2.744841	2.569489
2459534	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.780105	1.297995	-0.221014	-0.022706	0.766865	-0.307562	0.442688	-0.611784	0.7536	0.7250	0.3807	6.077816	5.261758
2459533	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.148131	5.111063	-0.424719	9.712925	2.519407	9.145750	0.811713	-1.527262	0.6935	0.6804	0.4102	2.665632	2.551505
2459532	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.463659	5.937881	-0.098148	12.539441	2.269716	12.026217	0.265235	-1.016069	0.6948	0.6786	0.4151	2.862306	2.717512
2459530	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.481899	0.961135	-0.494739	-0.028107	1.421765	-1.370735	0.076605	-0.861189	0.7164	0.6985	0.4120	2.721516	2.564621
2459527	dish_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	dish_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	dish_ok	100.00%	100.00%	100.00%	0.00%	-	-	0.326656	3.805045	-0.671668	4.602508	0.871940	0.746976	3.511695	0.583870	0.0358	0.0337	0.0027	nan	nan
2459508	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.476183	6.426748	-0.953560	7.580310	0.914036	6.942981	-0.120234	9.019459	0.8820	0.8968	0.2737	0.000000	0.000000
2459505	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.910062	4.156960	-0.086203	9.461267	2.577632	3.726159	1.295360	0.058657	0.8748	0.8338	0.2321	5.631293	5.164273

Antenna Season Report Notebook¶

Antenna 21 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 21, Node 2:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 21: 2459604

Antenna 21: 2459603

Antenna 21: 2459602

Antenna 21: 2459598

Antenna 21: 2459597

Antenna 21: 2459596

Antenna 21: 2459595

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Antenna 21: 2459589

Antenna 21: 2459588

Antenna 21: 2459587

Antenna 21: 2459586

Antenna 21: 2459585

Antenna 21: 2459584

Antenna 21: 2459583

Antenna 21: 2459582

Antenna 21: 2459581

Antenna 21: 2459580

Antenna 21: 2459579

Antenna 21: 2459578

Antenna 21: 2459577

Antenna 21: 2459576

Antenna 21: 2459575

Antenna 21: 2459574

Antenna 21: 2459573

Antenna 21: 2459572

Antenna 21: 2459571

Antenna 21: 2459570

Antenna 21: 2459569

Antenna 21: 2459566

Antenna 21: 2459565

Antenna 21: 2459564

Antenna 21: 2459563

Antenna 21: 2459562

Antenna 21: 2459561

Antenna 21: 2459560

Antenna 21: 2459559

Antenna 21: 2459558

Antenna 21: 2459556

Antenna 21: 2459552

Antenna 21: 2459551

Antenna 21: 2459549

Antenna 21: 2459542

Antenna 21: 2459541

Antenna 21: 2459540

Antenna 21: 2459536

Antenna 21: 2459535

Antenna 21: 2459534

Antenna 21: 2459533

Antenna 21: 2459532

Antenna 21: 2459530

Antenna 21: 2459527

Antenna 21: 2459524

Antenna 21: 2459523

Antenna 21: 2459522

Antenna 21: 2459513

Antenna 21: 2459508

Antenna 21: 2459506

Antenna 21: 2459505

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶